Contrastive learning has recently attracted great interest due to its empirical success in self-supervised representation learning in computer vision. In the last two years,
various contrastive methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]} for visual representations have been proposed. SimCLR [2]} presents three major findings to learn better representations, including composition of data augmentations, a learnable nonlinear transformation between the representation and the contrastive loss, and large batch size and training step.
Similar to SimCLR, we adopt the simple contrastive learning framework.
In order to integrate the contrative learning approach into the GAN-based models with simple implementation and small computational cost, our approach does not have the learnable nonlinear transformation or large batch size. We set the same training batch sizes as our baselines.
